The current gold standard for measuring blood glucose (sugar) levels is a test called HbA1c (glycated hemoglobin) along with fasting blood glucose and glucose tolerance tests or GTT (how fast blood glucose rises after ingesting a certain amount of sugar). HbA1c reflects average blood glucose levels over the previous two to three months. Blood glucose chemically links to hemoglobin producing glycated hemoglobin, therefore, the higher the average blood glucose the higher HbA1c.
These tests are limited to providing us with either a snapshot of blood glucose or an overall average. This is why glucose variability has recently emerged as a new and interesting metric for blood glucose monitoring. This is not only true in individuals with diabetes but also in people who may have impairment ifi glucose metabolism or even if those who simply want to avoid developing these conditions. A 2018 study, measured glucose variability in participants with normal blood glucose levels by standard measurements. 15% of them showed severe glucose variability reaching pre-diabetic ranges while 2% reached diabetic ranges despite normal HbA1c levels.
HbA1c and traditional measures of blood glucose might catch metabolic issues too late.
Glucose variability represents fluctuations in glucose over a period of time from its baseline. Glucose variability is difficult to measure but the advent of continuous glucose monitoring or CGM has made it much easier.
There are two types of glucose variability:
There are multiple proposed ways of measuring glucose variability. A popular measure is Standard deviation (SD) which represents how much glucose levels fluctuate over time from a given average. SD is often reflected in research and in practice by %CV or coefficient of variation (which is SD divided by the mean). The more stable the levels are over time, the lower CV is and therefore, glucose variability.
Glucose variability is obviously increased mostly after meals. The amount of carbohydrates in a food and its glycaemic index play the biggest role. Glucose variability can also be influenced by hormonal function, stress, illness and exercise. I will be writing a separate article on the relationship between glucose and exercise.
There is no consensus on what a good target for glucose variability should be. A normal %CV in healthy individuals is below 20. This means aiming for an SD that is less than 20% of the mean glucose. For instance, for someone with a mean glucose of 140 mg/dl, the target SD is 28 mg/dl or less.
Individuals with diabetes show increased glucose variability when compared to individuals without the disease. We also observe increased glucose variability with age even in individuals without diabetes. Increased glucose variability has also been found to be an independent predictor of increased complications in diabetics and all cause mortality. It has also been linked to insulin resistance and increased levels of inflammation.
Furthermore, individuals who end up developing diabetes, show an increased risk of cardiovascular disease even before the appearance of diabetes. This is not surprising as diabetes and increased risk of cardiovascular disease are part of the same picture of metabolic syndrome which is a term used to describe a cluster of conditions often seen together. These include impaired glucose metabolism, deranged cardiac risk markers, central obesity and high blood pressure. A study looking at glucose variability using a continuous glucose monitor (CGM) in three groups: subjects with metabolic syndrome, subjects with both diabetes and metabolic syndrome and subjects with neither.
There was a clear increase in glucose variability between the group with no medical conditions and the group with metabolic syndrome. There was a greater increase among the group with both diabetes and metabolic syndrome. This was true despite there being no similar difference in average glucose between the groups. Interestingly, some of the participants with metabolic syndrome showed a higher glucose variability than the diabetes plus metabolic syndrome group. This clearly suggests that there is a continuum of increased glucose variability which gets worse with higher degrees of metabolic dysregulation.
Intermittent exposure to high blood glucose, in comparison to constant exposure, has been shown to have deleterious effects on cells and tissues in experimental studies. Rapid fluctuations in glucose levels increase the production of superoxide radicals by mitochondria in cells. This increases oxidative stress and mitochondrial damage which have been linked to many age-related diseases. Studies measuring the effect of glucose infusions into otherwise healthy individuals showed that rapid rises in glucose can directly damage blood vessels by increasing adhesion of inflammatory molecules and immune cells to the lining of blood vessels. This is one of the mechanisms underlying atherosclerosis; the main cause of heart disease.
Studies have suggested that glucose variability can be used as a predictor of dips in blood glucose levels. In individuals with diabetes, episodes of hypoglycemia often follow fluctuations in blood glucose. A recent study published in Nature metabolism looked at glucose dips after meals in otherwise healthy individuals using CGMs. Interestingly, dips in blood glucose levels were found to be a strong predictor of subsequent hunger levels and increased energy intake 2-3 hours after the dip. The steeper the dip the more hungry we are afterwards. This was a stronger predictor than peak glucose level or overall glucose levels (area under the curve).
Although It is normal to have some excursions in blood glucose after meals, worsening glucose variability may be an early sign of metabolic dysfunction. There are no large trials of CGM use in non-diabetic individuals looking at long term health outcomes but the evidence does suggest that even in non-diabetics, it may be useful to control the following three metrics of blood glucose if we want to maintain our metabolic health in the long run:
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